Development of a Selection Tool for Choosing Decontamination Technology for Canadian Applications
Bibliographic record
Abstract
Abstract Decontamination is a major activity in decommissioning of Nuclear Power Plants. In efforts to reduce the overall volume of nuclear waste, retrieve reusable materials, and reduce the environmental impact, many different technologies have been developed/used in prior decommissioning projects and many more are being developed. However due to the amount of technologies available and the specific use cases for each, the ability to choose an appropriate and optimal technology is a challenge. An approach was adopted to develop a tool to assist in selection of decontamination technologies appropriate for Canadian Applications. The first step is the creation of a database to compile information of the different decontamination methods currently available in one location. The next step was the development of a software program to provide a search optimization for the database based on a set of initial user conditions. The program considers a radio-isotopic breakdown of a component as identified by the user and compares its concentration (Bq/g) to regulation limits set by the Canadian Nuclear Safety Commission (CSNC) for Unconditional Clearance Levels. Then, by using the CNSC guidelines, it will determine if the component is under Unconditional Levels or not. If the component is not, the code will calculate the minimum cumulative Decontamination Factor (DFR) required to make the component compliant with unconditional requirements. The software allows for users to plan their decontamination roadmap at a present state as well as a future state where natural decay opens up the ability for a wider range of decontamination technologies and for a combination of multiple components to use a given decontamination technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".